Automated analysis of the total choline resonance peak in breast proton magnetic resonance spectroscopy

The aim of the current study was to compare the performance of fully automated software with human expert interpretation of single-voxel proton magnetic resonance spectroscopy (1H-MRS) spectra in the assessment of breast lesions. Breast magnetic resonance imaging (MRI) (including contrast-enhanced T1-weighted, T2-weighted, and diffusion-weighted imaging) and 1H-MRS images of 74 consecutive patients were acquired on a 3-T positron emission tomography-MRI scanner then automatically imported into and analyzed by SpecTec-ULR 1.1 software (LifeTec Solutions GmbH). All ensuing 117 spectra were additionally independently analyzed and interpreted by two blinded radiologists. Histopathology of at least 24 months of imaging follow-up served as the reference standard. Nonparametric Spearman's correlation coefficients for all measured parameters (signal-to-noise ratio [SNR] and integral of total choline [tCho]), Passing and Bablok regression, and receiver operating characteristic analysis, were calculated to assess test diagnostic performance, as well as to compare automated with manual reading. Based on 117 spectra of 74 patients, the area under the curve for tCho SNR and integrals ranged from 0.768 to 0.814 and from 0.721 to 0.784 to distinguish benign from malignant tissue, respectively. Neither method displayed significant differences between measurements (automated vs. human expert readers, p > 0.05), in line with the results from the univariate Spearman's rank correlation coefficients, as well as the Passing and Bablok regression analysis. It was concluded that this pilot study demonstrates that 1H-MRS data from breast MRI can be automatically exported and interpreted by SpecTec-ULR 1.1 software. The diagnostic performance of this software was not inferior to human expert readers.

In Vitro MRS of Cells Treated with Trastuzumab at 1.5 Tesla

The aim of the study was to investigate the effect of Trastuzumab on the MCF-7 and CRL-2314 breast cancer cell lines. Additionally, an attempt was made to optimize magnetic resonance spectroscopy (MRS) for cell culture studies, with particular emphasis on the impact of treatment with Trastuzumab. The research materials included MCF-7 and CRL-2314 breast cancer cell lines. The study examined the response of these cell lines to treatment with Trastuzumab. The clinical magnetic resonance imaging (MRI) system, OPTIMA MR360 manufactured by GEMS, with a magnetic field induction of 1.5 T, was used. Due to the nature of the tested objects, their size and shape, it was necessary to design and manufacture additional receiving coils. They were used to image the tested cell cultures and record the spectroscopic signal. The spectra obtained by MRS were confirmed by NMR using a 300 MHz NMR Fourier 300 with the TopSpin 3.1 system from Bruker. The designed receiving coils allowed for conducting experiments with the cell lines in a satisfactory manner. These tests would not be possible using factory-delivered coils due to their parameters and the size of the test objects, whose volume did not exceed 1 mL. MRS studies revealed an increase in the metabolite at 1.9 ppm, which indicates the induction of histone acetylation. Changes in histone acetylation play a very important role in both cell development and differentiation processes. The use of Trastuzumab therapy in breast cancer cells increases the levels of acetylated histones. MRS studies and spectra obtained from the 300 MHz NMR system are consistent with the specificity inherent in both systems.

Multiparametric Approach to Breast Cancer With Emphasis on Magnetic Resonance Imaging in the Era of Personalized Breast Cancer Treatment

ABSTRACT

A multiparametric approach to breast cancer imaging offers the advantage of integrating the diverse contributions of various parameters. Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) is the most important MRI sequence for breast imaging. The vascularity and permeability of lesions can be estimated through the use of semiquantitative and quantitative parameters. The increased use of ultrafast DCE-MRI has facilitated the introduction of novel kinetic parameters. In addition to DCE-MRI, diffusion-weighted imaging provides information associated with tumor cell density, with advanced diffusion-weighted imaging techniques such as intravoxel incoherent motion, diffusion kurtosis imaging, and time-dependent diffusion MRI opening up new horizons in microscale tissue evaluation. Furthermore, T2-weighted imaging plays a key role in measuring the degree of tumor aggressiveness, which may be related to the tumor microenvironment. Magnetic resonance imaging is, however, not the only imaging modality providing semiquantitative and quantitative parameters from breast tumors. Breast positron emission tomography demonstrates superior spatial resolution to whole-body positron emission tomography and allows comparable delineation of breast cancer to MRI, as well as providing metabolic information, which often precedes vascular and morphological changes occurring in response to treatment. The integration of these imaging-derived factors is accomplished through multiparametric imaging. In this article, we explore the relationship among the key imaging parameters, breast cancer diagnosis, and histological characteristics, providing a technical and theoretical background for these parameters. Furthermore, we review the recent studies on the application of multiparametric imaging to breast cancer and the significance of the key imaging parameters.

Diagnostic accuracy of diffusion weighted imaging compared to magnetic resonance spectroscopy in differentiation of benign and malignant breast lesions: A systematic review and meta-analysis

OBJECTIVE

To compare the sensitivity and specificity of diffusion weighted imaging (DWI) and magnetic resonance spectroscopy (MRS) in the differentiation of benign and malignant breast lesions.

METHODS

Scopus, PubMed, and other registries were searched up to April 2023. We included diagnostic studies with DWI and MRS as index tests and histopathologic examination as the reference standard for differentiating benign and malignant breast lesions in adult females. We excluded studies involving healthy women, only breast cancer patients, and non-comparative diagnostic accuracy studies on either index test. The sensitivity and specificity of DWI and MRS were investigated and pooled using random-effect bivariate meta-analysis. Risk of bias was assessed using QUADAS-2. Evidence quality was summarized using GRADE.

RESULTS

Eight eligible studies involving 632 females and 687 breast lesions were identified. The pooled sensitivity and specificity of DWI were 92% (CI 85-96%) and 88% (CI 75-94%), respectively. The pooled sensitivity and specificity of MRS were 85% (CI 66-94%) and 85% (CI 77-91%), respectively. No significant difference was noted in the sensitivity (7%, CI -8-22%) and specificity (3%, CI -9-14%) between DWI and MRS.

CONCLUSIONS

In low to moderate quality evidence, DWI and MRS show comparable sensitivity and specificity in differentiating benign and malignant breast lesions.

Hyperpolarised 13C-MRI using 13C-pyruvate in breast cancer: A review

Tumour metabolism can be imaged with a novel imaging technique termed hyperpolarised carbon-13 (13C)-MRI using probes, i.e., endogenously found molecules that are labeled with 13C. Hyperpolarisation of the 13C label increases the sensitivity to a level that allows dynamic imaging of the distribution and metabolism of the probes. Dynamic imaging of [1-13C]pyruvate metabolism is of particular biological interest in cancer because of the Warburg effect resulting in the intratumoural accumulation of [1-13C]pyruvate and conversion to [1-13C]lactate. Numerous preclinical studies in breast cancer and other tumours have shown that hyperpolarised 13C-pyruvate has potential for metabolic phenotyping and response assessment at earlier timepoints than the current clinical imaging techniques allow. The clinical feasibility of hyperpolarised 13C-MRI after the injection of pyruvate in patients with breast cancer has now been demonstrated, with increased 13C-label exchange between pyruvate and lactate present in higher grade tumours with associated increased expression of the monocarboxylate transporter 1 (MCT1), the transmembrane transporter mediating intracellular pyruvate uptake, and lactate dehydrogenase (LDH) as the enzyme catalysing the conversion of pyruvate to lactate. Furthermore, a study in patients with breast cancer undergoing neoadjuvant chemotherapy suggested that early changes in 13C-label exchange can distinguish between patients who reach pathologic complete response (pCR) and those who do not. This review summarises the current literature on preclinical and clinical research on hyperpolarised 13C-MRI with [1-13C]-pyruvate in breast cancer imaging.

Ensemble Learning for Breast Cancer Lesion Classification: A Pilot Validation Using Correlated Spectroscopic Imaging and Diffusion-Weighted Imaging

The main objective of this work was to evaluate the application of individual and ensemble machine learning models to classify malignant and benign breast masses using features from two-dimensional (2D) correlated spectroscopy spectra extracted from five-dimensional echo-planar correlated spectroscopic imaging (5D EP-COSI) and diffusion-weighted imaging (DWI). Twenty-four different metabolite and lipid ratios with respect to diagonal fat peaks (1.4 ppm, 5.4 ppm) from 2D spectra, and water and fat peaks (4.7 ppm, 1.4 ppm) from one-dimensional non-water-suppressed (NWS) spectra were used as the features. Additionally, water fraction, fat fraction and water-to-fat ratios from NWS spectra and apparent diffusion coefficients (ADC) from DWI were included. The nine most important features were identified using recursive feature elimination, sequential forward selection and correlation analysis. XGBoost (AUC: 93.0%, Accuracy: 85.7%, F1-score: 88.9%, Precision: 88.2%, Sensitivity: 90.4%, Specificity: 84.6%) and GradientBoost (AUC: 94.3%, Accuracy: 89.3%, F1-score: 90.7%, Precision: 87.9%, Sensitivity: 94.2%, Specificity: 83.4%) were the best-performing models. Conventional biomarkers like choline, myo-Inositol, and glycine were statistically significant predictors. Key features contributing to the classification were ADC, 2D diagonal peaks at 0.9 ppm, 2.1 ppm, 3.5 ppm, and 5.4 ppm, cross peaks between 1.4 and 0.9 ppm, 4.3 and 4.1 ppm, 2.3 and 1.6 ppm, and the triglyceryl-fat cross peak. The results highlight the contribution of the 2D spectral peaks to the model, and they demonstrate the potential of 5D EP-COSI for early breast cancer detection.

Hyperpolarized Carbon-13 MRI in Breast Cancer

One of the hallmarks of cancer is metabolic reprogramming, including high levels of aerobic glycolysis (the Warburg effect). Pyruvate is a product of glucose metabolism, and ¹³C-MR imaging of the metabolism of hyperpolarized (HP) [1-¹³C]pyruvate (HP ¹³C-MRI) has been shown to be a potentially versatile tool for the clinical evaluation of tumor metabolism. Hyperpolarization of the ¹³C nuclear spin can increase the sensitivity of detection by 4-5 orders of magnitude. Therefore, following intravenous injection, the location of hyperpolarized ¹³C-labeled pyruvate in the body and its subsequent metabolism can be tracked using ¹³C-MRI. Hyperpolarized [¹³C]urea and [1,4-¹³C₂]fumarate are also likely to translate to the clinic in the near future as tools for imaging tissue perfusion and post-treatment tumor cell death, respectively. For clinical breast imaging, HP ¹³C-MRI can be combined with ¹H-MRI to address the need for detailed anatomical imaging combined with improved functional tumor phenotyping and very early identification of patients not responding to standard and novel neoadjuvant treatments. If the technical complexity of the hyperpolarization process and the relatively high associated costs can be reduced, then hyperpolarized ¹³C-MRI has the potential to become more widely available for large-scale clinical trials.

Advanced Magnetic Resonance Imaging Modalities for Breast Cancer Diagnosis: An Overview of Recent Findings and Perspectives

Breast cancer is the most prevalent cancer among women and the leading cause of death. Diffusion-weighted imaging (DWI) and diffusion tensor imaging (DTI) are advanced magnetic resonance imaging (MRI) procedures that are widely used in the diagnostic and treatment evaluation of breast cancer. This review article describes the characteristics of new MRI methods and reviews recent findings on breast cancer diagnosis. This review study was performed on the literature sourced from scientific citation websites such as Google Scholar, PubMed, and Web of Science until July 2021. All relevant works published on the mentioned scientific citation websites were investigated. Because of the propensity of malignancies to limit diffusion, DWI can improve MRI diagnostic specificity. Diffusion tensor imaging gives additional information about diffusion directionality and anisotropy over traditional DWI. Recent findings showed that DWI and DTI and their characteristics may facilitate earlier and more accurate diagnosis, followed by better treatment. Overall, with the development of instruments and novel MRI modalities, it may be possible to diagnose breast cancer more effectively in the early stages.

MRI Breast: Current Imaging Trends, Clinical Applications, and Future Research Directions

Magnetic Resonance Imaging (MRI) is the most sensitive and advanced imaging technique in diagnosing breast cancer and is essential in improving cancer detection, lesion characterization, and determining therapy response. In addition to the dynamic contrast-enhanced (DCE) technique, functional techniques such as magnetic resonance spectroscopy (MRS), diffusion-weighted imaging (DWI), diffusion kurtosis imaging (DKI), and intravoxel incoherent motion (IVIM) further characterize and differentiate benign and malignant lesions thus, improving diagnostic accuracy. There is now an increasing clinical usage of MRI breast, including screening in high risk and supplementary screening tools in average-risk patients. MRI is becoming imperative in assisting breast surgeons in planning breast-conserving surgery for preoperative local staging and evaluation of neoadjuvant chemotherapy response. Other clinical applications for MRI breast include occult breast cancer detection, investigation of nipple discharge, and breast implant assessment. There is now an abundance of research publications on MRI Breast with several areas that still remain to be explored. This review gives a comprehensive overview of the clinical trends of MRI breast with emphasis on imaging features and interpretation using conventional and advanced techniques. In addition, future research areas in MRI breast include developing techniques to make MRI more accessible and costeffective for screening. The abbreviated MRI breast procedure and an area of focused research in the enhancement of radiologists' work with artificial intelligence have high impact for the future in MRI Breast.

Correlated MR spectroscopic imaging of breast cancer to investigate metabolites and lipids: acceleration and compressed sensing reconstruction

Objectives

The main objective of this work was to detect novel biomarkers in breast cancer by spreading the MR spectra over two dimensions in multiple spatial locations using an accelerated 5D EP-COSI technology.

Methods

The 5D EP-COSI data were non-uniformly undersampled with an acceleration factor of 8 and reconstructed using group sparsity-based compressed sensing reconstruction. Different metabolite and lipid ratios were then quantified and statistically analyzed for significance. Linear discriminant models based on the quantified metabolite and lipid ratios were generated. Spectroscopic images of the quantified metabolite and lipid ratios were also reconstructed.

Results

The 2D COSY spectra generated using the 5D EP-COSI technique showed differences among healthy, benign, and malignant tissues in terms of their mean values of metabolite and lipid ratios, especially the ratios of potential novel biomarkers based on unsaturated fatty acids, myo-inositol, and glycine. It is further shown the potential of choline and unsaturated lipid ratio maps, generated from the quantified COSY signals across multiple locations in the breast, to serve as complementary markers of malignancy that can be added to the multiparametric MR protocol. Discriminant models using metabolite and lipid ratios were found to be statistically significant for classifying benign and malignant tumor from healthy tissues.

Conclusions

Accelerated 5D EP-COSI technique demonstrates the potential to detect novel biomarkers such as glycine, myo-inositol, and unsaturated fatty acids in addition to commonly reported choline in breast cancer, and facilitates metabolite and lipid ratio maps which have the potential to play a significant role in breast cancer detection.

Advances in knowledge

This study presents the first evaluation of a multidimensional MR spectroscopic imaging technique for the detection of potentially novel biomarkers based on glycine, myo-inositol, and unsaturated fatty acids, in addition to commonly reported choline. Spatial mapping of choline and unsaturated fatty acid ratios with respect to water in malignant and benign breast masses are also shown. These metabolic characteristics may serve as additional biomarkers for improving the diagnostic and therapeutic evaluation of breast cancer.

The potential of predictive and prognostic breast MRI (P2-bMRI)

Magnetic resonance imaging (MRI) is an important part of breast cancer diagnosis and multimodal workup. It provides unsurpassed soft tissue contrast to analyse the underlying pathophysiology, and it is adopted for a variety of clinical indications. Predictive and prognostic breast MRI (P2-bMRI) is an emerging application next to these indications. The general objective of P2-bMRI is to provide predictive and/or prognostic biomarkers in order to support personalisation of breast cancer treatment. We believe P2-bMRI has a great clinical potential, thanks to the in vivo examination of the whole tumour and of the surrounding tissue, establishing a link between pathophysiology and response to therapy (prediction) as well as patient outcome (prognostication). The tools used for P2-bMRI cover a wide spectrum: standard and advanced multiparametric pulse sequences; structured reporting criteria (for instance BI-RADS descriptors); artificial intelligence methods, including machine learning (with emphasis on radiomics data analysis); and deep learning that have shown compelling potential for this purpose. P2-bMRI reuses the imaging data of examinations performed in the current practice. Accordingly, P2-bMRI could optimise clinical workflow, enabling cost savings and ultimately improving personalisation of treatment. This review introduces the concept of P2-bMRI, focusing on the clinical application of P2-bMRI by using semantic criteria.

Radiologic-pathologic correlation in breast cancer: do MRI biomarkers correlate with pathologic features and molecular subtypes?

Background

Breast cancer (BC) includes different pathological and molecular subtypes. This study aimed to investigate whether multiparametric magnetic resonance imaging (mpMRI) could reliably predict the molecular status of BC, comparing mpMRI features with pathological and immunohistochemical results.

Methods

This retrospective study included 156 patients with an ultrasound-guided biopsy-proven BC, who underwent breast mpMRI (including diffusion-weighted imaging) on a 3-T scanner from 2017 to 2020. Histopathological analyses were performed on the surgical specimens. Kolmogorov–Smirnov Z, χ², and univariate and multivariate logistic regression analyses were performed.

Results

Fifteen patients were affected with ductal carcinoma in situ, 122 by invasive carcinoma of no special type, and 19 with invasive lobular carcinoma. Out of a total of 141 invasive cancers, 45 were luminal A-like, 54 luminal B-like, 5 human epidermal growth factor receptor 2 (HER2) positive, and 37 triple negative. The regression analyses showed that size < 2 cm predicted luminal A-like status (p = 0.025), while rim enhancement (p < 0.001), intralesional necrosis (p = 0.001), peritumoural oedema (p < 0.001), and axillary adenopathies (p = 0.012) were negative predictors. Oppositely, round shape (p = 0.001), rim enhancement (p < 0.001), intralesional necrosis (p < 0.001), and peritumoural oedema (p < 0.001) predicted triple-negative status.

Conclusions

mpMRI has been confirmed to be a valid noninvasive predictor of BC subtypes, especially luminal A and triple negative. Considering the central role of pathology in BC diagnosis and immunohistochemical profiling in the current precision medicine era, a detailed radiologic-pathologic correlation seems vital to properly evaluate BC.

Small Study Effects in Diagnostic Imaging Accuracy: A Meta-Analysis

IMPORTANCE

Small study effects are the phenomena that studies with smaller sample sizes tend to report larger and more favorable effect estimates than studies with larger sample sizes.

OBJECTIVE

To evaluate the presence and extent of small study effects in diagnostic imaging accuracy meta-analyses.

DATA SOURCES

A search was conducted in the PubMed database for diagnostic imaging accuracy meta-analyses published between 2010 and 2019.

STUDY SELECTION

Meta-analyses with 10 or more studies of medical imaging diagnostic accuracy, assessing a single imaging modality, and providing 2 × 2 contingency data were included. Studies that did not assess diagnostic accuracy of medical imaging techniques, compared 2 or more imaging modalities or different methods of 1 imaging modality, were cost analyses, used predictive or prognostic tests, did not provide individual patient data, or were network meta-analyses were excluded.

DATA EXTRACTION AND SYNTHESIS

Data extraction was performed in accordance with the PRISMA guidelines.

MAIN OUTCOMES AND MEASURES

The diagnostic odds ratio (DOR) was calculated for each primary study using 2 × 2 contingency data. Regression analysis was used to examine the association between effect size estimate and precision across meta-analyses.

RESULTS

A total of 31 meta-analyses involving 668 primary studies and 80 206 patients were included. Fixed effects analysis produced a regression coefficient for the natural log of DOR against the SE of the natural log of DOR of 2.19 (95% CI, 1.49-2.90; P < .001), with computed tomography as the reference modality. Interaction test for modality and SE of the natural log of DOR did not depend on modality (Wald statistic P = .50). Taken together, this analysis found an inverse association between effect size estimate and precision that was independent of imaging modality. Of 26 meta-analyses that formally assessed for publication bias using funnel plots and statistical tests for funnel plot asymmetry, 21 found no evidence for such bias.

CONCLUSIONS AND RELEVANCE

This meta-analysis found evidence of widespread prevalence of small study effects in the diagnostic imaging accuracy literature. One likely contributor to the observed effects is publication bias, which can undermine the results of many meta-analyses. Conventional methods for detecting funnel plot asymmetry conducted by included studies appeared to underestimate the presence of small study effects. Further studies are required to elucidate the various factors that contribute to small study effects.

Magnetic Resonance Imaging (MRI) and MR Spectroscopic Methods in Understanding Breast Cancer Biology and Metabolism

A common malignancy that affects women is breast cancer. It is the second leading cause of cancer-related death among women. Metabolic reprogramming occurs during cancer growth, invasion, and metastases. Functional magnetic resonance (MR) methods comprising an array of techniques have shown potential for illustrating physiological and molecular processes changes before anatomical manifestations on conventional MR imaging. Among these, in vivo proton (1H) MR spectroscopy (MRS) is widely used for differentiating breast malignancy from benign diseases by measuring elevated choline-containing compounds. Further, the use of hyperpolarized 13C and 31P MRS enhanced the understanding of glucose and phospholipid metabolism. The metabolic profiling of an array of biological specimens (intact tissues, tissue extracts, and various biofluids such as blood, urine, nipple aspirates, and fine needle aspirates) can also be investigated through in vitro high-resolution NMR spectroscopy and high-resolution magic angle spectroscopy (HRMAS). Such studies can provide information on more metabolites than what is seen by in vivo MRS, thus providing a deeper insight into cancer biology and metabolism. The analysis of a large number of NMR spectral data sets through multivariate statistical methods classified the tumor sub-types. It showed enormous potential in the development of new therapeutic approaches. Recently, multiparametric MRI approaches were found to be helpful in elucidating the pathophysiology of cancer by quantifying structural, vasculature, diffusion, perfusion, and metabolic abnormalities in vivo. This review focuses on the applications of NMR, MRS, and MRI methods in understanding breast cancer biology and in the diagnosis and therapeutic monitoring of breast cancer.

Is Elevated Choline on Magnetic Resonance Spectroscopy a Reliable Marker of Breast Lesion Malignancy?

Background

Contemporary magnetic resonance imaging (MRI) of the breast represents a powerful diagnostic modality for cancer detection, with excellent sensitivity and high specificity. Magnetic resonance spectroscopy (MRS) is being explored as an additional tool for improving specificity in breast cancer detection, using multiparametric MRI. The aim of this study was to examine the possibility of 1H-MRS to discriminate malignant from benign breast lesions, using elevated choline (Cho) peak as an imaging biomarker.

Methods

A total of 60 patients were included in this prospective study: 30 with malignant (average age, 55.2 years; average lesion size, 35 mm) and 30 with benign breast lesions (average age, 44.8 years; average lesion size, 20 mm), who underwent multiparametric MRI with multivoxel 3D ¹H-MRS on a 1.5-T scanner in a 3-year period. Three patients with benign breast lesions were excluded from the study. All lesions were histologically verified. Peaks identified on ¹H-MRS were lipid (0.9, 2.3, 2.8, and 5.2 ppm), choline (3.2 ppm), and water peaks (4.7 ppm). Sensitivity and specificity, as well as positive and negative predictive values, were defined using ROC curves. Cohen's Kappa test of inter-test reliability was performed [testing the agreement between ¹H-MRS and histologic finding, and ¹H-MRS and MR mammography (MRM)].

Results

Choline peak was elevated in 24/30 malignant lesions and in 20/27 benign breast lesions. The sensitivity of ¹H-MRS was 0.8, specificity was 0.741, positive predictive value was 0.774, and negative predictive value was 0.769. Area under ROC was 0.77 (CI 0.640-0.871). Inter-test reliability between ¹H-MRS and histologic finding was 0.543 (moderate agreement) and that between ¹H-MRS and MRM was 0.573 (moderate agreement). False-negative findings were most frequently observed in invasive lobular cancers, while false-positive findings were most frequently observed in adenoid fibroadenomas.

Conclusion

Although elevation of the choline peak has a good sensitivity and specificity in breast cancer detection, both are significantly lower than those of multiparametric MRM. Inclusion of spectra located on tumor margins as well as analysis of lipid peaks could aid both sensitivity and specificity. An important ratio of false-positive and false-negative findings in specific types of breast lesions (lobular cancer and adenoid fibroadenoma) suggests interpreting these lesions with a caveat.

[Influences of Tumor Volume and FWHM of the Water Peak and T2* Value of Water on the Detection Rate of the Choline Peaks in Proton MR Spectroscopy of Breast Cancer at 3.0 T-MRI]

In proton magnetic resonance (MR) spectroscopy (¹H-MRS) of the breast cancer, choline peak could be detected. The purpose of this study was to evaluate the influences of the tumor volume, full width at half maximum (FWHM) of the water peak (FWHM), and T₂* value of water (T₂* value) on the detection rate of the choline peaks at 3.0 T-MRI. We measured FWHM and T₂* value in 109 cases, and we evaluated the effect of tumor volume on the detection rate of the choline peaks and the effect of FWHM and T₂* value on the detection of choline peaks. In ¹H-MRS of breast cancer at 3.0 T-MRI, the detection rate of the choline peaks improved as the tumor volume was larger. As a shimming environment when acquiring ¹H-MRS of breast cancer, FWHM is preferably 57.4 Hz or less and T₂* value should be 11 ms or more, and T₂* value has a great influence on the detection rate of the choline peaks.

The use of hyperpolarised 13C-MRI in clinical body imaging to probe cancer metabolism

Metabolic reprogramming is one of the hallmarks of cancer and includes the Warburg effect, which is exhibited by many tumours. This can be exploited by positron emission tomography (PET) as part of routine clinical cancer imaging. However, an emerging and alternative method to detect altered metabolism is carbon-13 magnetic resonance imaging (MRI) following injection of hyperpolarised [1-13C]pyruvate. The technique increases the signal-to-noise ratio for the detection of hyperpolarised 13C-labelled metabolites by several orders of magnitude and facilitates the dynamic, noninvasive imaging of the exchange of 13C-pyruvate to 13C-lactate over time. The method has produced promising preclinical results in the area of oncology and is currently being explored in human imaging studies. The first translational studies have demonstrated the safety and feasibility of the technique in patients with prostate, renal, breast and pancreatic cancer, as well as revealing a successful response to treatment in breast and prostate cancer patients at an earlier stage than multiparametric MRI. This review will focus on the strengths of the technique and its applications in the area of oncological body MRI including noninvasive characterisation of disease aggressiveness, mapping of tumour heterogeneity, and early response assessment. A comparison of hyperpolarised 13C-MRI with state-of-the-art multiparametric MRI is likely to reveal the unique additional information and applications offered by the technique.

Bibliometric evaluation of systematic review and meta analyses published in the top 5 "high-impact" radiology journals

INTRODUCTION

Meta-analyses provide high-level evidence and understanding their trends may provide understanding of the field as a whole. Bibliometric analysis was undertaken to understand research trends in a particular field or subfield and to assess citation as a measure of impact.

METHODS

All journals categorised as "Radiology, Nuclear Medicine & Medical Imaging" under the Web of Science subject category were included. After analyzing impact factors of the journals in up to 2018, the top five journals were identified. The retrieved results were ordered by citation count based on Web of Science and Scopus. Specific parameters regarding the title, journal, publication year, authors, country of origin, institution and university, field of study and funding sources were analyzed.

RESULTS

A total of 139 articles were identified. The mean number of citations per article was 25.3 and 22.6 in Scopus and Web of Science respectively, with four articles receiving 100 or more citations. European Radiology had the greatest number of top cited articles (n = 68; 49%). Most number of articles originated from South Korea (n = 60; 43%) and the commonest field of focus with the most common being oncology (n = 51; 27%).

CONCLUSION

The top 5 high impact journals published a large number of meta-analysis and systematic reviews. The greatest number of top-cited articles were from South Korea, shifting away from the United States. Large number of studies focused on oncologic imaging, consistent with recent trends towards development of imaging biomarkers and personalized medicine. Author H index did not predict citation number or density.

Current and Emerging Magnetic Resonance-Based Techniques for Breast Cancer

Breast cancer is the most commonly diagnosed cancer among women worldwide, and early detection remains a principal factor for improved patient outcomes and reduced mortality. Clinically, magnetic resonance imaging (MRI) techniques are routinely used in determining benign and malignant tumor phenotypes and for monitoring treatment outcomes. Static MRI techniques enable superior structural contrast between adipose and fibroglandular tissues, while dynamic MRI techniques can elucidate functional characteristics of malignant tumors. The preferred clinical procedure-dynamic contrast-enhanced MRI-illuminates the hypervascularity of breast tumors through a gadolinium-based contrast agent; however, accumulation of the potentially toxic contrast agent remains a major limitation of the technique, propelling MRI research toward finding an alternative, noninvasive method. Three such techniques are magnetic resonance spectroscopy, chemical exchange saturation transfer, and non-contrast diffusion weighted imaging. These methods shed light on underlying chemical composition, provide snapshots of tissue metabolism, and more pronouncedly characterize microstructural heterogeneity. This review article outlines the present state of clinical MRI for breast cancer and examines several research techniques that demonstrate capacity for clinical translation. Ultimately, multi-parametric MRI-incorporating one or more of these emerging methods-presently holds the best potential to afford improved specificity and deliver excellent accuracy to clinics for the prediction, detection, and monitoring of breast cancer.

Differential diagnosis of plasma cell mastitis and invasive ductal carcinoma using multiparametric MRI

Background

Evaluate the potential of multiparametric magnetic resonance imaging (MRI) for the differential diagnosis of plasma cell mastitis (PCM) and invasive ductal carcinoma (IDC).

Methods

A total of 465 female patients, including 197 PCM (42.4%) and 268 IDC (57.6%), were examined using breast MRI scanning using routine sequences, dynamic contrast-enhanced MRI (DCE-MRI), diffusion-weighted imaging (DWI) and MR spectroscopy (MRS). The MRI features of PCM and IDC lesions were analyzed and compared to the histological results.

Results

Compared to IDC, the PCM lesions were more frequent in the subareolar regions, hyperintense on T2WI (P<0.01) and showed an initial signal increase ≤90%, a persistent and plateau pattern of time-intensity curves, non-mass enhancement, multiple rim enhancements, central hyperintensity on DWI, a higher ADC value, and total choline (tCho) peak negative and tCho peak integral <29.95 AU (P<0.01). The following breast-associated findings were also observed frequently in PCM: Ipsilateral breast enlargement, nipple retraction, skin thickening, peripheral edema and axillary lymphadenopathy. However, no significant difference was observed between the two groups for the shape, border and adjacent vessel signs of the lesion.

Conclusions

Some of the MRI features of PCM and IDC lesions were different. An integrated analysis of these multiparametric MRI features can thus assist in the differential diagnosis of PCM and IDC lesions.

Clinical relevance of total choline (tCho) quantification in suspicious lesions on multiparametric breast MRI

Purpose

To assess the additional value of quantitative tCho evaluation to diagnose malignancy and lymph node metastases in suspicious lesions on multiparametric breast MRI (mpMRI, BI-RADS 4, and BI-RADS 5).

Methods

One hundred twenty-one patients that demonstrated suspicious multiparametric breast MRI lesions using DCE, T2w, and diffusion-weighted (DW) images were prospectively enrolled in this IRB-approved study. All underwent single-voxel proton MR spectroscopy (¹H-MRS, point-resolved spectroscopy sequence, TR 2000 ms, TE 272 ms) with and without water suppression. The total choline (tCho) amplitude was measured and normalized to millimoles/liter according to established methodology by two independent readers (R1, R2). ROC-analysis was employed to predict malignancy and lymph node status by tCho results.

Results

One hundred three patients with 74 malignant and 29 benign lesions had full ¹H-MRS data. The area under the ROC curve (AUC) for prediction of malignancy was 0.816 (R1) and 0.809 (R2). A cutoff of 0.8 mmol/l tCho could diagnose malignancy with a sensitivity of > 95%. For prediction of lymph node metastases, tCho measurements achieved an AUC of 0.760 (R1) and 0.788 (R2). At tCho levels < 2.4 mmol/l, no metastatic lymph nodes were found.

Conclusion

Quantitative tCho evaluation from ¹H-MRS allowed diagnose malignancy and lymph node status in breast lesions suspicious on multiparametric breast MRI. tCho therefore demonstrated the potential to downgrade suspicious mpMRI lesions and stratify the risk of lymph node metastases for improved patient management.

Key Points

• Quantitative tCho evaluation can distinguish benign from malignant breast lesions suspicious after multiparametric MRI assessment.

• Quantitative tCho levels are associated with lymph node status in breast cancer.

• Quantitative tCho levels are higher in hormonal receptor positive compared to hormonal receptor negative lesions.

Electronic supplementary material

The online version of this article (10.1007/s00330-020-06678-z) contains supplementary material, which is available to authorized users.

Cancer metabolism in a snapshot: MRS(I)

The contribution of MRS(I) to the in vivo evaluation of cancer-metabolism-derived metrics, mostly since 2016, is reviewed here. Increased carbon consumption by tumour cells, which are highly glycolytic, is now being sampled by ¹³ C magnetic resonance spectroscopic imaging (MRSI) following the injection of hyperpolarized [1-¹³ C] pyruvate (Pyr). Hot-spots of, mostly, increased lactate dehydrogenase activity or flow between Pyr and lactate (Lac) have been seen with cancer progression in prostate (preclinical and in humans), brain and pancreas (both preclinical) tumours. Therapy response is usually signalled by decreased Lac/Pyr ¹³ C-labelled ratio with respect to untreated or non-responding tumour. For therapeutic agents inducing tumour hypoxia, the ¹³ C-labelled Lac/bicarbonate ratio may be a better metric than the Lac/Pyr ratio. ³¹ P MRSI may sample intracellular pH changes from brain tumours (acidification upon antiangiogenic treatment, basification at fast proliferation and relapse). The steady state tumour metabolome pattern is still in use for cancer evaluation. Metrics used for this range from quantification of single oncometabolites (such as 2-hydroxyglutarate in mutant IDH1 glial brain tumours) to selected metabolite ratios (such as total choline to N-acetylaspartate (plain ratio or CNI index)) or the whole ¹ H MRSI(I) pattern through pattern recognition analysis. These approaches have been applied to address different questions such as tumour subtype definition, following/predicting the response to therapy or defining better resection or radiosurgery limits.

In vivo MR spectroscopy for breast cancer diagnosis

Breast cancer is a significant health concern in females, worldwide. In vivo proton (1H) MR spectroscopy (MRS) has evolved as a non-invasive tool for diagnosis and for biochemical characterization of breast cancer. Water-to-fat ratio, fat and water fractions and choline containing compounds (tCho) have been identified as diagnostic biomarkers of malignancy. Detection of tCho in normal breast tissue of volunteers and in lactating females limits the use of tCho as a diagnostic marker. Technological developments like high-field scanners, multi channel coils, pulse sequences with water and fat suppression facilitated easy detection of tCho. Also, quantification of tCho and its cut-off for objective assessment of malignancy have been reported. Meta-analysis of in vivo 1H MRS studies have documented the pooled sensitivities and the specificities in the range of 71-74% and 78-88%, respectively. Inclusion of MRS has been shown to enhance the diagnostic specificity of MRI, however, detection of tCho in small sized lesions (≤1 cm) is challenging even at high magnetic fields. Potential of MRS in monitoring the effect of chemotherapy in breast cancer has also been reported. This review briefly presents the potential clinical role of in vivo 1H MRS in the diagnosis of breast cancer, its current status and future developments.

Magnetic resonance spectroscopy may serve as a presurgical predictor of somatostatin analog therapy response in patients with growth hormone-secreting pituitary macroadenomas

PURPOSE

Somatostatin analogs (SSAs) are considered one of the most effective medical treatments for patients with growth hormone-secreting pituitary adenomas (GH-PAs). The postoperative electron microscopy (EM) pathological subtype and SSTR2 expression in the tumor are the most established predictors of patient response to SSA therapy. The aim of this study was to evaluate how will magnetic resonance spectroscopy (MRS) measurements before surgery predict the EM pathological subtypes and SSTR2 expression of tumors, and thereby serve as an indicator for the therapeutic sensitivity to SSAs of patients with GH-PAs.

METHODS

Eighteen patients with GH pituitary macroadenomas who underwent transsphenoidal surgery were included in this retrospective study. The preoperative MRS data and T2 signal intensity were obtained from patients by 1.5 T MR spectroscopy of the sellar mass. The EM pathological subtypes of tumors were determined after surgery through examination of cell granulations. The expressions of somatostatin receptor 2 (SSTR2), SSTR5, P21, P27, and Ki-67 were evaluated by immunohistochemistry.

RESULTS

The MRS parameters that were found to significantly predict the EM pathological subtypes of tumors, as calculated by the receiver operating characteristic curve, were the choline (Ch) value at 3140.5 MR units (sensitivity 69.2%, specificity 100%) and the choline/creatine (Ch/Cr) ratio at 1.27 (sensitivity 92.3%, specificity 100%). Further, the Ch/Cr ratio, but not other MRS data, was shown to negatively correlate with the expression of SSTR2 (P = 0.02). The Ch/Cr ratio was also found to positively correlate with the Ki-67 value (P < 0.05) and T2 signal (P < 0.05), but not with other factors that were examined in this study. Moreover, the Ch/Cr ratio could predict the EM pathological subtypes of tumors with an accuracy of 83.3% (5/6) for patients with an isointense T2 signal.

CONCLUSION

The Ch/Cr ratio by MRS could effectively predict the tumor subtype and was significantly correlated with the expression of SSTR2, which was consistent with other predictors. It was also able to distinguish the patients with isointense T2 signals. Our results provide a potentially new and non-invasive method to predict the response to SSAs in patients with GH pituitary macroadenomas.

How rapid advances in imaging are defining the future of precision radiation oncology

Imaging has an essential role in the planning and delivery of radiotherapy. Recent advances in imaging have led to the development of advanced radiotherapy techniques—including image-guided radiotherapy, intensity-modulated radiotherapy, stereotactic body radiotherapy and proton beam therapy. The optimal use of imaging might enable higher doses of radiation to be delivered to the tumour, while sparing normal surrounding tissues. In this article, we review how the integration of existing and novel forms of computed tomography, magnetic resonance imaging and positron emission tomography have transformed tumour delineation in the radiotherapy planning process, and how these advances have the potential to allow a more individualised approach to the cancer therapy. Recent data suggest that imaging biomarkers that assess underlying tumour heterogeneity can identify areas within a tumour that are at higher risk of radio-resistance, and therefore potentially allow for biologically focussed dose escalation. The rapidly evolving concept of adaptive radiotherapy, including artificial intelligence, requires imaging during treatment to be used to modify radiotherapy on a daily basis. These advances have the potential to improve clinical outcomes and reduce radiation-related long-term toxicities. We outline how recent technological advances in both imaging and radiotherapy delivery can be combined to shape the future of precision radiation oncology.

Proton MR spectroscopy in the breast: Technical innovations and clinical applications

Proton magnetic resonance spectroscopy (MRS) is a promising noninvasive diagnostic technique for investigation of breast cancer metabolism. Spectroscopic imaging data may be obtained following contrast-enhanced MRI by applying the point-resolved spectroscopy sequence (PRESS) or the stimulated echo acquisition mode (STEAM) sequence from the MR voxel encompassing the breast lesion. Total choline signal (tCho) measured in vivo using either a qualitative or quantitative approach has been used as a diagnostic test in the workup of malignant breast lesions. In addition to tCho metabolites, other relevant metabolites, including multiple lipids, can be detected and monitored. MRS has been heavily investigated as an adjunct to morphologic and dynamic MRI to improve diagnostic accuracy in breast cancer, obviating unnecessary benign biopsies. Besides its use in the staging of breast cancer, other promising applications have been recently investigated, including the assessment of treatment response and therapy monitoring. This review provides guidance on spectroscopic acquisition and quantification methods and highlights current and evolving clinical applications of proton MRS. Level of Evidence 5 Technical Efficacy: Stage 5 J. Magn. Reson. Imaging 2019.

Diffusion Kurtosis at 3.0T as an in vivo Imaging Marker for Breast Cancer Characterization: Correlation With Prognostic Factors

BACKGROUND

Diffusion-kurtosis imaging (DKI) has preliminarily shown promise as a relatively new MRI technique to provide useful information regarding breast lesions, but the diagnostic performance of DKI has not been fully evaluated.

PURPOSE

To compare the diagnostic accuracy of DKI, diffusion-weighted imaging (DWI), dynamic contrast-enhanced (DCE)-MRI) and proton MR spectroscopy (¹ H-MRS) in differentiating malignant from benign breast lesions independently or jointly, and explore the correlation between DKI-derived parameters and prognostic factors.

STUDY TYPE

Prospective.

SUBJECTS

Seventy-one patients with breast lesions (50 malignant, 26 benign).

SEQUENCE

DKI, DWI, DCE-MRI, and ¹ H-MRS were performed at 3.0T.

ASSESSMENT

Mean kurtosis (MK), mean diffusivity (MD), apparent diffusion coefficient (ADC), BI-RADS category, and choline peaks were analyzed by two experienced radiologists.

STATISTICAL TESTS

Student's t-test was used for continuous variables; receiver operating characteristic (ROC) analysis for assessing the diagnostic accuracy of imaging parameters; Spearman or Pearson correlations for assessing the associations between imaging parameters and prognostic factors.

RESULTS

MK exhibited higher area under the curves (AUCs) for differentiating malignant from benign lesions than did MD, ADC, DCE, and tCho (0.979 vs. 0.928, 0.911, 0.777, and 0.833, respectively, P < 0.05). MK showed a positive association with Ki-67 expression (r = 0.508) and histologic grades (r = 0.551), whereas MD and ADC were negatively correlated with Ki-67 expression (r = -0.416 and r = -0.458) and histologic grades (r = -0.411 and r = -0.319). Moreover, MK showed relatively higher AUCs compared with MD and ADC in detecting breast cancers with lymph nodal involvement, histologic grades, and Ki-67 expression.

DATA CONCLUSION

MK has higher diagnostic accuracy compared with ADC, DCE, and tCho regarding detection of breast cancer. Moreover, DKI shows promise as a quantitative imaging technique for characterizing breast lesions, highlighting the potential utility of MK as a promising imaging marker for predicting tumor aggressiveness.

LEVEL OF EVIDENCE

2 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2019;49:845-856.

Diffusion-weighted imaging (DWI) with apparent diffusion coefficient (ADC) mapping as a quantitative imaging biomarker for prediction of immunohistochemical receptor status, proliferation rate, and molecular subtypes of breast cancer

BACKGROUND

Diffusion-weighted imaging (DWI) with apparent diffusion coefficient (ADC) mapping is one of the most useful additional MRI parameters to improve diagnostic accuracy and is now often used in a multiparameric imaging setting for breast tumor detection and characterization.

PURPOSE

To evaluate whether different ADC metrics can also be used for prediction of receptor status, proliferation rate, and molecular subtype in invasive breast cancer.

STUDY TYPE

Retrospective.

SUBJECTS

In all, 107 patients with invasive breast cancer met the inclusion criteria (mean age 57 years, range 32-87) and underwent multiparametric breast MRI.

FIELD STRENGTH/SEQUENCE

3 T, readout-segmented echo planar imaging (rsEPI) with IR fat suppression, dynamic contrast-enhanced (DCE) T₁ -weighted imaging, T₂ -weighted turbo-spin echo (TSE) with fatsat.

ASSESSMENT

Two readers independently drew a region of interest on ADC maps on the whole tumor (WTu), and on its darkest part (DpTu). Minimum, mean, and maximum ADC values of both WTu and DpTu were compared for receptor status, proliferation rate, and molecular subtypes.

STATISTICAL TESTS

Wilcoxon rank sum, Mann-Whitney U-tests for associations between radiologic features and histopathology; histogram and q-q plots, Shapiro-Wilk's test to assess normality, concordance correlation coefficient for precision and accuracy; receiver operating characteristics curve analysis.

RESULTS

Estrogen receptor (ER) and progesterone receptor (PR) status had significantly different ADC values for both readers. Maximum WTu (P = 0.0004 and 0.0005) and mean WTu (P = 0.0101 and 0.0136) were significantly lower for ER-positive tumors, while PR-positive tumors had significantly lower maximum WTu values (P = 0.0089 and 0.0047). Maximum WTu ADC was the only metric that was significantly different for molecular subtypes for both readers (P = 0.0100 and 0.0132) and enabled differentiation of luminal tumors from nonluminal (P = 0.0068 and 0.0069) with an area under the curve of 0.685 for both readers.

DATA CONCLUSION

Maximum WTu ADC values may be used to differentiate luminal from other molecular subtypes of breast cancer.

LEVEL OF EVIDENCE

3 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2019;50:836-846.

Changes in total choline concentration in the breast of healthy fertile young women in relation to menstrual cycle or use of oral contraceptives: a 3-T 1H-MRS study

Background

To evaluate changes in total choline (tCho) absolute concentration ([tCho]) in the breast of healthy fertile women in relation to menstrual cycle (MC) or use of oral contraceptives (OC).

Methods

After institutional review board approval, we prospectively evaluated 40 healthy fertile volunteers: 20 with physiological MC, aged 28 ± 3 years (mean ± standard deviation; nOC group); 20 using OC, aged 26 ± 3 years (OC group). Hormonal assays and water-suppressed single-voxel 3-T proton magnetic resonance spectroscopy (¹H-MRS) were performed on MC days 7, 14, and 21 in the nOC group and only on MC day 14 in the OC group. [tCho] was measured versus an external phantom. Mann-Whitney U test and Spearman coefficient were used; data are given as median and interquartile interval.

Results

All spectra had good quality. In the nOC group, [tCho] (mM) did not change significantly during MC: 0.8 (0.3–2.4) on day 7, 0.9 (0.4–1.2) on day 14, and 0.4 (0.2–0.8) on day 21 (p = 0.963). In the OC group, [tCho] was 0.7 (0.2–1.7) mM. The between-groups difference was not significant on all days (p ≥ 0.411). All hormones except prolactin changed during MC (p ≤ 0.024). In the OC group, [tCho] showed a borderline correlation with estradiol (r = 0.458, p = 0.056), but no correlation with other hormones (p ≥ 0.128). In the nOC group, [tCho] negatively correlated with prolactin (r = -0.587, p = 0.006) on day 7; positive correlation was found with estradiol on day 14 (r = 0.679, p = 0.001).

Conclusions

A tCho peak can be detected in the normal mammary gland using 3-T ¹H-MRS. The [tCho] in healthy volunteers was 0.4–0.9 mM, constant over the MC and independent of OC use.

Multiparametric MRI model with dynamic contrast-enhanced and diffusion-weighted imaging enables breast cancer diagnosis with high accuracy

Background

The MRI Breast Imaging‐Reporting and Data System (BI‐RADS) lexicon recommends that a breast MRI protocol contain T₂‐weighted and dynamic contrast‐enhanced (DCE) MRI sequences. The addition of diffusion‐weighted imaging (DWI) significantly improves diagnostic accuracy. This study aims to clarify which descriptors from DCE‐MRI, DWI, and T₂‐weighted imaging are most strongly associated with a breast cancer diagnosis.

Purpose/Hypothesis

To develop a multiparametric MRI (mpMRI) model for breast cancer diagnosis incorporating American College of Radiology (ACR) BI‐RADS recommended descriptors for breast MRI with DCE, T₂‐weighted imaging, and DWI with apparent diffusion coefficient (ADC) mapping.

Study Type

Retrospective.

Subjects

In all, 188 patients (mean 51.6 years) with 210 breast tumors (136 malignant and 74 benign) who underwent mpMRI from December 2010 to September 2014.

Field Strength/Sequence

IR inversion recovert DCE‐MRI dynamic contrast‐enhanced magnetic resonance imaging VIBE Volume‐Interpolated‐Breathhold‐Examination FLASH turbo fast‐low‐angle‐shot TWIST Time‐resolved angiography with stochastic Trajectories.

Assessment

Two radiologists in consensus and another radiologist independently evaluated the mpMRI data. Characteristics for mass (n = 182) and nonmass (n = 28) lesions were recorded on DCE and T₂‐weighted imaging according to BI‐RADS, as well as DWI descriptors. Two separate models were analyzed, using DCE‐MRI BI‐RADS descriptors, T₂‐weighted imagines, and ADCmean as either a continuous or binary form using a previously published ADC cutoff value of ≤1.25 × 10⁻³ mm²/sec for differentiation between benign and malignant lesions. Histopathology was the standard of reference.

Statistical Tests

χ² test, Fisher's exact test, Kruskal–Wallis test, Pearson correlation coefficient, multivariate logistic regression analysis, Hosmer–Lemeshow test of goodness‐of‐fit, receiver operating characteristics analysis.

Results

In Model 1, ADCmean (P = 0.0031), mass margins with DCE (P = 0.0016), and delayed enhancement with DCE (P = 0.0016) were significantly and independently associated with breast cancer diagnosis; Model 2 identified ADCmean (P = 0.0031), mass margins with DCE (P = 0.0012), initial enhancement (P = 0.0422), and delayed enhancement with DCE (P = 0.0065) to be significantly independently associated with breast cancer diagnosis. T₂‐weighted imaging variables were not included in the final models.

Data Conclusion

mpMRI with DCE‐MRI and DWI with ADC mapping enables accurate breast cancer diagnosis. A model using quantitative and qualitative descriptors from DCE‐MRI and DWI identifies breast cancer with a high diagnostic accuracy. T₂‐weighted imaging does not significantly contribute to breast cancer diagnosis.

Level of Evidence: 3

Technical Efficacy: Stage 2

J. Magn. Reson. Imaging 2019;49:864–874.

Is there a correlation between 3T multiparametric MRI and molecular subtypes of breast cancer?

OBJECTIVES

To test whether 3 T multiparametric magnetic resonance imaging (mMRI) provides information related to molecular subtypes of breast cancer.

METHODS

Women with mammographic or US findings of breast lesions (BI-RADS 4-5) underwent 3 T mMRI (DCE, DWI and MR spectroscopy). The histological type of breast cancer was assessed. Estrogen-receptor (ER), progesterone-receptor (PgR), Ki-67 status and HER-2 expression, assessed by immunohistochemistry (IHC), defined four molecular subtypes: Luminal-A, Luminal-B, HER2-enriched and triple-negative. Non-parametric tests (Kruskal-Wallis, k-sample equality of medians, and Mann-Whitney), logistic regression or ANOVA, and a multivariate analysis were performed to investigate correlations between the four molecular subtypes and mMRI (lesion volume, margins or distribution, enhancement pattern, ADC, type of kinetic curve, and total choline (tCho) signal-to-noise-ratio (SNR)). A ROC analysis was finally performed to test the diagnostic power of a multivariate logistic regression model.

RESULTS

433 patients (453 lesions) were considered. Volume was smaller in Luminal-B and larger in triple-negative tumours compared to the other subtypes combined. Margins were significantly correlated to Luminal-A and Luminal-B. The type of curve was significantly correlated to Luminal-A. ADC values were higher in Luminal-A. tCho SNR was higher in triple-negative tumours. The ROC analysis showed that the area under the curve (AUC) significantly improved when multiple MRI features were used compared to individual parameters.

CONCLUSIONS

A significant correlation was found between some MRI features and molecular subtypes of breast tumours. A multiparametric approach improved the diagnostic power of MRI. However, further research is needed in order to predict the molecular subtype on the sole basis of mMRI.

Clinical role of breast MRI now and going forward

Magnetic resonance imaging (MRI) is a well-established method in breast imaging, with manifold clinical applications, including the non-invasive differentiation between benign and malignant breast lesions, preoperative staging, detection of scar versus recurrence, implant assessment, and the evaluation of high-risk patients. At present, dynamic contrast-enhanced MRI is the most sensitive imaging technique for breast cancer diagnosis, and provides excellent morphological and to some extent also functional information. To compensate for the limited functional information, and to increase the specificity of MRI while preserving its sensitivity, additional functional parameters such as diffusion-weighted imaging and apparent diffusion coefficient mapping, and MR spectroscopic imaging have been investigated and implemented into the clinical routine. Several additional MRI parameters to capture breast cancer biology are still under investigation. MRI at high and ultra-high field strength and advances in hard- and software may also further improve this imaging technique. This article will review the current clinical role of breast MRI, including multiparametric MRI and abbreviated protocols, and provide an outlook on the future of this technique. In addition, the predictive and prognostic value of MRI as well as the evolving field of radiogenomics will be discussed.

Total choline quantification measured by 1H MR spectroscopy as early predictor of response after neoadjuvant treatment for locally advanced breast cancer: The impact of immunohistochemical status

BACKGROUND

Validation of new biomarkers is essential for the early evaluation of neoadjuvant treatments.

PURPOSE

To determine whether measurements of total choline (tCho) by 1H spectroscopy could predict morphological or pathological complete response (pCR) of neoadjuvant treatment and whether breast cancer subgroups are related to prediction accuracy.

STUDY TYPE

Prospective, nonrandomized, monocentric, diagnostic study.

POPULATION

Sixty patients were initially included with 39 women participating in the final cohort.

FIELD STRENGTH/SEQUENCE

A 1.5T scanner was used for acquisition and MRS was performed using the syngo GRACE sequence.

ASSESSMENT

MRS and MRI examinations were performed at baseline (TP1), 24-72 hours after first chemotherapy (TP2), after the end of anthracycline treatment (TP3), and MRI only after the end of taxane treatment (TP4). Early (EMR) and late (LMR) morphological response were defined as %ΔDmax13 or %ΔDmax14, respectively. Responders were patients with %ΔDmax >30. Pathological complete response (pCR) patients achieved a residual cancer burden score of 0.

STATISTICAL TESTS

T-test, receiver operating characteristic (ROC) curves, multiple regression, logistic regression, one-way analysis of variance (ANOVA) analysis were used for the analysis.

RESULTS

At TP1 there was a significant difference between response groups for tCho1 concerning EMR prediction (P = 0.05) and pCR (P < 0.05) and for K_ep 1 (P = 0.03) concerning LMR prediction. At TP2, no modification of tCho and other parameters could predict response. At TP3, ΔtCho, ΔDmax, and ΔVol could predict LMR (P < 0.05 for all parameters), pCR (P < 0.05 for all parameters), and ΔK^trans could predict only pCR (P = 0.04). Logistic regression at baseline showed the highest area under the curve (AUC) of 0.9 for prediction of pCR. The triple negative (TN) subgroup showed significantly higher tCho at baseline (P = 0.02) and higher ΔtCho levels at TP3 (P < 0.05).

DATA CONCLUSION

Baseline measurements of tCho in combination with clinicopathological criteria could predict non-pCR with a high AUC. Furthermore, tCho quantification for prediction of pCR was more sensitive for TN tumors.

LEVEL OF EVIDENCE

1 Technical Efficacy: Stage 4 J. Magn. Reson. Imaging 2018;48:982-993.

[Technique of proton and phosphorous MR spectroscopy]

CLINICAL/METHODICAL ISSUE

Magnetic resonance spectroscopy (MRS) is an important non-invasive method that can reveal the concentration and spatial distribution of particular biochemically relevant tissue metabolites.

STANDARD RADIOLOGICAL METHODS

Proton MRS is routinely applicable in the clinical setting providing good quality results even with a moderate magnetic field strength of 1.5 T. Relative values of metabolite concentrations are mostly used for the assessment of metabolic disorders.

METHODICAL INNOVATIONS

Absolute quantification of metabolites can be achieved by means of internal or external reference scans. Phosphorous MRS extends the range of detectable molecules to energy and cell membrane metabolism.

PERFORMANCE

The lower detection limit of metabolite concentrations is in the range of some mmol/kg. Depending on the magnetic field strength, MRS enables a spatial resolution of a few milliliters.

ACHIEVEMENTS

The use of phosphorous MRS is considerably limited because higher field strengths of at least 3.0 T and additional expensive hardware for signal processing are required.

Study of lipid metabolism by estimating the fat fraction in different breast tissues and in various breast tumor sub-types by in vivo 1H MR spectroscopy

PURPOSE

To evaluate the utility of fat fraction (FF) for the differentiation of different breast tissues and in various breast tumor subtypes using in vivo proton (¹H) magnetic resonance spectroscopy (MRS).

METHODS

¹H MRS was performed on 68 malignant, 35 benign, and 30 healthy volunteers at 1.5 T. Malignant breast tissues of patients were characterized into different subtypes based on the differences in the expression of hormone receptors and the FF was calculated. Further, the sensitivity and specificity of FF to differentiate malignant from benign and from normal breast tissues of healthy volunteers was determined using receiver operator curve (ROC) analysis.

RESULTS

A significantly lower FF of malignant (median 0.12; range 0.01-0.70) compared to benign lesions (median 0.28; range 0.02-0.71) and normal breast tissue of healthy volunteers (median 0.39; range 0.06-0.76) was observed. No significant difference in FF was seen between benign lesions and normal breast tissues of healthy volunteers. Sensitivity and specificity of 75% and 68.6%, respectively was obtained to differentiate malignant from benign lesions. For the differentiation of malignant from healthy breast tissues, 76% sensitivity and 74.5% specificity was achieved. Higher FF was seen in patients with ER-/PR- status as compared to ER+/PR+ patients. Similarly, FF of HER2neu+ tumors were significantly higher than in HER2neu- breast tumors.

CONCLUSION

The results showed the potential of in vivo ¹H MRS in providing insight into the changes in the fat content of different types of breast tissues and in various breast tumor subtypes.

A survey by the European Society of Breast Imaging on the utilisation of breast MRI in clinical practice

OBJECTIVES

While magnetic resonance imaging (MRI) is considered a helpful diagnostic tool in breast imaging, discussions are ongoing about appropriate protocols and indications. The European Society of Breast Imaging (EUSOBI) launched a survey to evaluate the utilisation of breast MRI in clinical practice.

METHODS

An online survey reviewed by the EUSOBI board and committees was distributed amongst members. The questions encompassed: training and experience; annual breast MRI and MRI-guided-intervention workload; examination protocols; indications; reporting habits and preferences. Data were summarised and subgroups compared using χ² test.

RESULTS

Of 647 EUSOBI members, 177 (27.4%) answered the survey. The majority were radiologists (90.5%), half of them based in academic centres (51.9%). Common indications for MRI included cancer staging, treatment monitoring, high-risk screening and problem-solving, and differed significantly between countries (p≤0.03). Structured reporting and BI-RADS were mostly used. Breast radiologists with ≤10 years of experience preferred inclusion of additional techniques, such as T2/STIR (p=0.03) and DWI (p=0.08) in the scan protocol. MRI-guided interventions were performed by a minority of participants (35.4%).

CONCLUSIONS

The utilisation of breast MRI in clinical practice is generally in line with international recommendations. There are substantial differences between countries. MRI-guided interventions and functional MRI parameters are not widely available.

KEY POINTS

• MRI is commonly used for the detection and characterisation of breast lesions. • Clinical practice standards are generally in line with current recommendations. • Standardised criteria and diagnostic categories (mainly BI-RADS) are widely adopted. • Younger radiologists value additional techniques, such as T2/STIR and DWI. • MRI-guided breast biopsy is not widely available.

[Multiparametric and molecular imaging of breast tumors with MRI and PET/MRI]

CLINICAL/METHODICAL ISSUE

Magnetic resonance imaging (MRI) of the breast is an indispensable tool in breast imaging for many indications. Several functional parameters with MRI and positron emission tomography (PET) have been assessed for imaging of breast tumors and their combined application is defined as multiparametric imaging. Available data suggest that multiparametric imaging using different functional MRI and PET parameters can provide detailed information about the hallmarks of cancer and may provide additional specificity.

STANDARD RADIOLOGICAL METHODS

Multiparametric and molecular imaging of the breast comprises established MRI parameters, such as dynamic contrast-enhanced MRI, diffusion-weighted imaging (DWI), MR proton spectroscopy ((1)H-MRSI) as well as combinations of radiological and MRI techniques (e. g. PET/CT and PET/MRI) using radiotracers, such as fluorodeoxyglucose (FDG).

METHODICAL INNOVATIONS

Multiparametric and molecular imaging of the breast can be performed at different field-strengths (range 1.5-7 T). Emerging parameters comprise novel promising techniques, such as sodium imaging ((23)Na MRI), phosphorus spectroscopy ((31)P-MRSI), chemical exchange saturation transfer (CEST) imaging, blood oxygen level-dependent (BOLD) and hyperpolarized MRI as well as various specific radiotracers.

ACHIEVEMENTS

Multiparametric and molecular imaging has multiple applications in breast imaging. Multiparametric and molecular imaging of the breast is an evolving field that will enable improved detection, characterization, staging and monitoring for personalized medicine in breast cancer.

Magnetic Resonance Spectroscopy and its Clinical Applications: A Review

In vivo NMR spectroscopy is known as magnetic resonance spectroscopy (MRS). MRS has been applied as both a research and a clinical tool in order to detect visible or nonvisible abnormalities. The adaptability of MRS allows a technique that can probe a wide variety of metabolic uses across different tissues. Although MRS is mostly applied for brain tissue, it can be used for detection, localization, staging, tumour aggressiveness evaluation, and tumour response assessment of breast, prostate, hepatic, and other cancers. In this article, the medical applications of MRS in the brain, including tumours, neural and psychiatric disorder studies, breast, prostate, hepatic, gastrointestinal, and genitourinary investigations have been reviewed.

Breast Tissue Metabolism by Magnetic Resonance Spectroscopy

Metabolic alterations are known to occur with oncogenesis and tumor progression. During malignant transformation, the metabolism of cells and tissues is altered. Cancer metabolism can be studied using advanced technologies that detect both metabolites and metabolic activities. Identification, characterization, and quantification of metabolites (metabolomics) are important for metabolic analysis and are usually done by nuclear magnetic resonance (NMR) or by mass spectrometry. In contrast to the magnetic resonance imaging that is used to monitor the tumor morphology during progression of the disease and during therapy, in vivo NMR spectroscopy is used to study and monitor tumor metabolism of cells/tissues by detection of various biochemicals or metabolites involved in various metabolic pathways. Several in vivo, in vitro and ex vivo NMR studies using ¹H and ³¹P magnetic resonance spectroscopy (MRS) nuclei have documented increased levels of total choline containing compounds, phosphomonoesters and phosphodiesters in human breast cancer tissues, which is indicative of altered choline and phospholipid metabolism. These levels get reversed with successful treatment. Another method that increases the sensitivity of substrate detection by using nuclear spin hyperpolarization of ¹³C-lableled substrates by dynamic nuclear polarization has revived a great interest in the study of cancer metabolism. This review discusses breast tissue metabolism studied by various NMR/MRS methods.

[Systematic errors in clinical studies : A comprehensive survey]

Systematic errors and related phenomena represent an intrinsic challenge to the quality of clinical research. As a consequence even otherwise methodologically demanding studies may produce results that systematically differ from the true values. Systematic errors relating to investigative medicine are divided into six groups according to their affiliation with the consecutive chronological sections of the study. Bias can occur in preliminary literature research in the field, specifying the study design and selecting the study sample, measuring exposure and outcome, analyzing the data, interpreting the analyses and publishing the results. The most important systematic errors that concern diagnostic and interventional studies are created by access to the data of previous tests, calculated study design, preselection of the participants, comparison with non-contemporaneous controls, antedating the time of diagnosis and overdiagnosis of slowly progressive forms of diseases examined. Checking the measured values often leads to a mosaic of several biases with one being more or less dominant. Even by exercising due care in the preparation and performance of the study, the majority of distortions cannot be eliminated but only diminished. It is essential to consider each detected bias as a potential full or partial argument in support of an observed correlation. The control of systematic errors and related phenomena is both a significant element of the discussion of the study report and a key element for assessment of its scientific value.

Proton magnetic resonance spectroscopy in oncology: the fingerprints of cancer?

Abnormal metabolism is a key tumor hallmark. Proton magnetic resonance spectroscopy (1H-MRS) allows measurement of metabolite concentration that can be utilized to characterize tumor metabolic changes. 1H-MRS measurements of specific metabolites have been implemented in the clinic. This article performs a systematic review of image acquisition and interpretation of 1H-MRS for cancer evaluation, evaluates its strengths and limitations, and correlates metabolite peaks at 1H-MRS with diagnostic and prognostic parameters of cancer in different tumor types.

[Systematic errors in clinical studies : A comprehensive survey]

Systematic errors and related phenomena represent an intrinsic challenge to the quality of clinical research. As a consequence even otherwise methodologically demanding studies may produce results that systematically differ from the true values. Systematic errors relating to investigative medicine are divided into six groups according to their affiliation with the consecutive chronological sections of the study. Bias can occur in preliminary literature research in the field, specifying the study design and selecting the study sample, measuring exposure and outcome, analyzing the data, interpreting the analyses and publishing the results. The most important systematic errors that concern diagnostic and interventional studies are created by access to the data of previous tests, calculated study design, preselection of the participants, comparison with non-contemporaneous controls, antedating the time of diagnosis and overdiagnosis of slowly progressive forms of diseases examined. Checking the measured values often leads to a mosaic of several biases with one being more or less dominant. Even by exercising due care in the preparation and performance of the study, the majority of distortions cannot be eliminated but only diminished. It is essential to consider each detected bias as a potential full or partial argument in support of an observed correlation. The control of systematic errors and related phenomena is both a significant element of the discussion of the study report and a key element for assessment of its scientific value.